Contactless Transfer Learning Based Apnea Detection System for Wi-Fi CSI Networks

Sleep apnea syndrome is a common sleep disorder that can lead to a variety of diseases. The traditional diagnostic method, polysomnography (PSG), is time-consuming, expensive, and inconvenient for patients. In this paper, we proposed the transfer learning based apnea detection (TLAD) system as a non-contact based method utilizing the channel state information (CSI) from commercial Wi-Fi devices. In order to reduce the overhead of collecting CSI data and improving efficiency during training process, the transfer learning technique is applied to establish pre-trained model by utilizing open source contact-based thoracic movement data. Moreover, existing research works detect apnea based on breathing pauses and shallow breathing periods, which are not effective to identify complex apnea characteristics. This potential drawback is overcome in proposed TLAD system since both CSI amplitude and frequency features are extracted for apnea classification. Our experimental results showed that the TLAD system achieves an F1-score of 90.1, which is superior to other existing methods.